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Article of Volume 14, Issue 3, September 2019

A handheld classroom dashboard: Teachers’ perspectives on the use of real-time collaborative learning analytics

Author: Roberto Martinez-Maldonado

Abstract: In Computer-Supported Collaborative Learning (CSCL) classrooms it may be challenging for teachers to keep awareness of certain aspects of the learning process of each small group or assess whether the enactment of the class script deviates from the original plan. Orchestration tools, aimed at supporting the management of the increasing uncertainty and complexity of CSCL classrooms, have been emerging in response. Similarly, learning analytics innovations hold the promise of empowering teachers by making certain aspects of the classroom visible and by providing information that can prompt actionable responses. However, the active role that data may play in teachers’ decision-making and orchestration processes is still not well understood. This paper investigates the perspectives of teachers who used a real-time analytics tool to support the orchestration of a CSCL classroom. A longitudinal study was conducted with a handheld dashboard deployed in a multi-display collaborative classroom during one full academic term. The dashboard showed real-time information about group participation and task progress; the current state of the CSCL script; and a set of text notifications informing teachers of potential students’ misconceptions automatically detected. The study involved four teachers conducting 72 classroom sessions during 10 weeks with a total of 150 students. The teachers’ perspectives discussed in this paper portray the promises and challenges of introducing new technologies aimed at enhancing orchestration and awareness in a CSCL classroom.

Keywords: Small group collaboration, Multi-display, Classroom ecologies, Learning analytics, Dashboard, Data visualisation, Learning design, Tabletops

Citation: Martinez-Maldonado, R. (2019) A handheld classroom dashboard: Teachers’ perspectives on the use of real-time collaborative learning analytics. ijcscl 14 (3), pp. 383-411

DOI: 10.1007/s11412-019-09308-z

Preprint: Acrobat-PDF maldonado_14_3.pdf

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